Accounting requirements for donor‐imposed restrictions and the restricted funds of not‐for‐profit organisations
Bibliographic record
Abstract
Not‐for‐profit organisations often experience accounting problems when dealing with the restrictions that donors impose on how the organisations may spend funds. Part of the accountability and stewardship that the managements of not‐for‐profit organisations assume is adhering to the wishes of donors and reporting compliance with restrictions. Fund accounting is a general phenomenon among not‐for‐profit organisations. The use of different funds usually stems from the restrictions imposed by donors, and funds are used to account for restricted resources. Separate funds are often used to separate restricted funds from other funds in these organisations, and to present information to the users of financial statements, indicating that the organisation has indeed complied with donor‐imposed restrictions. This article discusses the principles of some accounting standards already issued specifically for not‐for‐profit organisations in the United States of America, Canada, the United Kingdom and Australia, and presents the results of empirical research on how donor‐imposed restrictions could be recorded in the financial statements of not‐for‐profit organisations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.071 | 0.192 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".